Results Notebook

1 Overview

1.1 Participants

A total of 1146 participants were recruited through a survey posted on Prolific. 184 were excluded as they did not complete the survey, and 98 were excluded as they are vegan/vegetarian, and 5 were excluded for indicating that their results should not be included in the analysis. 13 were excluded for failing to select the correct response in an attention check. The final sample (N = 846) ranged in age 18 to 79 (M_age = 37.21, SD = 13.58). The participants were predominantly female (56.58%). The participants received £0.35 ($0.45) for successfully completing the task.

1.2 Randomization check

A preliminary randomization check was conducted. The check revealed no systematic differences between the three conditions in gender, age, political position, and nationality (all p’s > .05).

Table 1.1: Randomisation check

Item	Dynamic	Static	No norm	Significance test
Age (years)	37.34 \(\pm\) 14.22	37.90 \(\pm\) 12.97	36.40 \(\pm\) 13.55	\(F(2, 843) = 0.89\), \(\mathit{MSE} = 184.49\), \(p = .409\)
Gender (%)	1 (39.49%) 2 (60.14%) 3 (0.36%)	1 (41.9%) 2 (58.1%)	1 (48.25%) 2 (51.05%) 3 (0.7%)	\(\chi^2(4, n = 846) = 6.92\), \(p = .140\)
Political position	3.47 \(\pm\) 1.22	3.54 \(\pm\) 1.26	3.45 \(\pm\) 1.26	\(F(2, 843) = 0.39\), \(\mathit{MSE} = 1.55\), \(p = .679\)
Nationality (%)	1 (84.06%) 2 (3.62%) 3 (9.78%) 4 (0.72%) NA (1.81%)	1 (80.99%) 2 (5.28%) 3 (8.45%) 4 (1.41%) NA (3.87%)	1 (80.77%) 2 (5.94%) 3 (8.74%) 4 (1.75%) NA (2.8%)	\(\chi^2(6, n = 846) = 3.23\), \(p = .779\)

1.3 Correlations

Table 1.2: Means, Standard Deviations, Reliabilities, and Inter-Correlations Among Study Measures

	Alpha	M	SD	1	2	3	4	5	6
Interest		3.58	1.82
Attitude	0.90	4.64	1.29	.80**
Intention	0.98	4.22	1.80	.83**	.82**
Expectation	0.99	3.93	1.75	.80**	.80**	.92**
Intent/expectation composite		4.08	1.74	.83**	.83**	.98**	.98**
Perception of change		5.14	0.90	.27**	.26**	.27**	.25**	.27**
Preconformity		4.18	1.20	.44**	.40**	.39**	.37**	.39**	.37**

2 Confirmatory analyses

2.1 1. Does making dynamic norms about reduced meat consumption in the UK salient lead to higher interest in reducing their meat consumption (compared to static norm)?

2.1.1 Effect of condition on interest in reducing meat consumption

Sparkman and Walton (2017) found effects of dynamic norms on interest in reducing meat consumption ranging from M_diff = 0.60 – 0.78. Thus, the rough mean difference between dynamic and static norms expected in the sample is 0.69 on a 7 point Likert scale. Thus, I modeled H1 as a half-normal with an SD of 0.69. The plausible maximum effect was set at 1.38.

Figure 2.1: World cloud of participants response to text

Table 2.1:

Most frequent words in text

word	freq
health	216.00
environment	193.00
animals	144.00
climate	67.00
awareness	64.00
concerns	64.00
impact	56.00
media	50.00
vegan	43.00
better	35.00
change	33.00
vegetarian	32.00
production	31.00
bad	30.00
benefits	30.00
becoming	28.00
food	28.00
reduce	28.00
diet	27.00
cost	26.00

The mean interest for participants in the dynamic norm condition was M = 3.64 (SD = 1.83), and the mean interest in the static norm condition was M = 3.68 (SD = 1.84). The mean interest in the no norm condition was M = 3.41 (SD = 1.77).

There was no difference in interest in reducing meat consumption between the dynamic norm (M = 3.64, SD = 1.83) and static norm (M = 3.68, SD = 1.84) conditions, \(\Delta M = -0.03\), 95% CI \([-0.34\), \(0.27]\), \(t(843) = -0.23\), \(p = .821\), d = -0.02, \(B_{\text{HN}(0, 0.69)}\) = 0.18, RR[0.65, 2].

Participants in the no-norm control condition showed the least interest in reducing meat consumption (M = 3.41, SD = 1.77) and did not differ from those in the dynamic-norm condition \(\Delta M = 0.23\), 95% CI \([-0.07\), \(0.53]\), \(t(843) = 1.52\), \(p = .130\), d = 0.13, or the static-norm condition \(\Delta M = 0.27\), 95% CI \([-0.03\), \(0.57]\), \(t(843) = 1.76\), \(p = .080\), d = 0.15. There was also no difference between the dynamic-norm condition and a combination of the control and static-norm conditions \(\Delta M = 0.10\), 95% CI \([-0.16\), \(0.36]\), \(t(843) = 0.74\), \(p = .458\).

Table 2.2:

Meat consumption by condition contrasts

	contrast	\(\Delta M\)	95% CI	\(t(843)\)	\(p\)
DYST	Dynamic, static	-0.03	\([-0.34\), \(0.27]\)	-0.23	.821
DYNO	Dynamic, control	0.23	\([-0.07\), \(0.53]\)	1.52	.130
STNO	Static, control	0.27	\([-0.03\), \(0.57]\)	1.76	.080
DYCONT	Dynamic, both	0.10	\([-0.16\), \(0.36]\)	0.74	.458
EXPNO	Norms, control	-0.25	\([-0.51\), \(0.01]\)	-1.89	.059

2.1.2 Effect of demographic variables and condition on interest

Political left-wing participants were more interested than were right wing participants, \(t(844) = -7.23\), \(p < .001\), and women were more interested than were men, \(\Delta M = -0.50\), 95% CI \([-0.75\), \(-0.26]\), \(t(781.22) = -4.00\), \(p < .001\). When we controlled for these factors, the effect of the dynamic-norm condition (compared with that of the static-norm condition) on interest in eating less meat was \(b = -0.27\), 95% CI \([-1.25\), \(0.72]\), \(t(553) = -0.53\), \(p = .594\).

Table 2.3:

Regression coefficients of demographic variables on interest

predictor	\(b\)	95% CI	\(t(553)\)	\(p\)
Intercept	4.61	\([3.90\), \(5.31]\)	12.80	< .001
Condition	-0.27	\([-1.25\), \(0.72]\)	-0.53	.594
Gender	0.53	\([0.10\), \(0.96]\)	2.43	.015
Political position	-0.37	\([-0.54\), \(-0.19]\)	-4.14	< .001
Condition \(\times\) Gender	-0.01	\([-0.61\), \(0.59]\)	-0.03	.976
Condition \(\times\) Political position	0.09	\([-0.15\), \(0.33]\)	0.77	.443

2.2 2. Will participants in the dynamic norm condition be more likely (than static norm and control) to predict a future decrease in meat consumption in the UK?

I modeled H2 using a half-normal distribution with a mean of 0 and SD of M_diff = 0.40. The plausible maximum effect was set at twice the predicted effect of M_diff = 0.80. A Bayes factor was calculated for each test.

Table 2.4: Expectations of future meat consumption

		Future Norm		Preconformity		Combined
Condition	\(n\)	\(M\)	\(SD\)	\(M\)	\(SD\)	\(M\)	\(SD\)
Dynamic	276	5.26	0.93	4.35	1.18	4.81	0.88
Static	284	5.20	0.85	4.24	1.19	4.72	0.85
No norm	286	4.97	0.89	3.95	1.18	4.46	0.84

2.2.0.1 Measure of perception of change: “In the next 5 years, I expect meat consumption in the UK to…”

There was no evidence one way or another for an effect of dynamic norm condition on expectations about future meat consumption, \(\Delta M = 0.06\), 95% CI \([-0.08\), \(0.21]\), \(t(843) = 0.85\), \(p = .397\), d = 0.07, \(B_{\text{HN}(0, 0.40)}\) = 0.42, RR[0.05, 0.8]

Table 2.5: Perception change contrasts

	contrast	estimate	ci	statistic	p.value
DYST	Dynamic, static	0.06	\([-0.08\), \(0.21]\)	0.85	.397
DYNO	Dynamic, control	0.30	\([0.15\), \(0.44]\)	3.94	< .001
STNO	Static, control	0.23	\([0.09\), \(0.38]\)	3.11	.002
DYCONT	Dynamic, both	0.18	\([0.05\), \(0.31]\)	2.75	.006
EXPNO	Norms, control	-0.26	\([-0.39\), \(-0.14]\)	-4.08	< .001

2.2.0.2 Measure of preconformity: “In the foreseeable future, to what extent do you think that many people will make an effort to eat less meat?”

There was no evidence one way or the other for there being a difference in anticipation that many people would make an effort to reduce their meat consumption in the future between the dynamic norm (M = 4.35, SD = 1.18) and static norm (M = 4.24, SD = 1.19) conditions, \(\Delta M = 0.11\), 95% CI \([-0.09\), \(0.31]\), \(t(843) = 1.08\), \(p = .279\), d = 0.09, \(B_{\text{HN}(0, 0.40)}\) = 0.72, RR[0.05, 1.5].

Table 2.6: Preconformity contrasts

	contrast	estimate	ci	statistic	p.value
DYST	Dynamic, static	0.11	\([-0.09\), \(0.31]\)	1.08	.279
DYNO	Dynamic, control	0.40	\([0.20\), \(0.60]\)	4.00	< .001
STNO	Static, control	0.29	\([0.10\), \(0.49]\)	2.94	.003
DYCONT	Dynamic, both	0.25	\([0.08\), \(0.43]\)	2.93	.004
EXPNO	Norms, control	-0.35	\([-0.52\), \(-0.18]\)	-4.02	< .001

2.2.0.3 Combined

There was no evidence one way or the other for there being a difference in anticipation that many people would make an effort to reduce their meat consumption in the future between the dynamic norm (M = 4.81, SD = 0.88) and static norm (M = 4.72, SD = 0.85) conditions, \(\Delta M = 0.09\), 95% CI \([-0.06\), \(0.23]\), \(t(843) = 1.19\), \(p = .235\), d = 0.10, \(B_{\text{HN}(0, 0.40)}\) = 0.62, RR[0.05, 1.25].

Table 2.7: Combined contrasts

	contrast	estimate	ci	statistic	p.value
DYST	Dynamic, static	0.09	\([-0.06\), \(0.23]\)	1.19	.235
DYNO	Dynamic, control	0.35	\([0.21\), \(0.49]\)	4.81	< .001
STNO	Static, control	0.26	\([0.12\), \(0.40]\)	3.65	< .001
DYCONT	Dynamic, both	0.22	\([0.09\), \(0.34]\)	3.45	.001
EXPNO	Norms, control	-0.31	\([-0.43\), \(-0.18]\)	-4.89	< .001

3 Secondary analyses

3.1 1. Will there be a difference in perceptions of current static norm across the dynamic and static norm conditions?

The SESOI for percentage difference is ± 5%. The SESOI for mean difference on the Likert scale is ± 0.5.

TOST results: t-value lower bound: 129.959 p-value lower bound: 0e+00 t-value upper bound: \(-23.608\) p-value upper bound: \(2.99\times10^{-86}\) degrees of freedom : 557.66

Equivalence bounds (raw scores): low eqbound: \(-5\) high eqbound: 5

TOST confidence interval: lower bound 90% CI: 3.355 upper bound 90% CI: 3.57

NHST confidence interval: lower bound 95% CI: 3.335 upper bound 95% CI: 3.591

Equivalence Test Result: The equivalence test was significant, t(557.66) = \(-23.608\), p = \(2.99\times10^{-86}\), given equivalence bounds of \(-5\) and 5 (on a raw scale) and an alpha of 0.05. Null Hypothesis Test Result: The null hypothesis test was significant, t(557.66) = 53.175, p = \(1.51\times10^{-220}\), given an alpha of 0.05. Based on the equivalence test and the null-hypothesis test combined, we can conclude that the observed effect is statistically different from zero and statistically equivalent to zero.

3.1.1 2. Will there be a difference in how meat consumption is construed across the dynamic and static norm conditions?

The SESOI for difference in number of meals is ± 2 meals.

TOST results: t-value lower bound: 15.086 p-value lower bound: \(1.12\times10^{-43}\) t-value upper bound: \(-13.542\) p-value upper bound: \(1.25\times10^{-36}\) degrees of freedom : 557.55

Equivalence bounds (raw scores): low eqbound: \(-5\) high eqbound: 5

TOST confidence interval: lower bound 90% CI: \(-0.306\) upper bound 90% CI: 0.845

NHST confidence interval: lower bound 95% CI: \(-0.417\) upper bound 95% CI: 0.956

Equivalence Test Result: The equivalence test was significant, t(557.55) = \(-13.542\), p = \(1.25\times10^{-36}\), given equivalence bounds of \(-5\) and 5 (on a raw scale) and an alpha of 0.05. Null Hypothesis Test Result: The null hypothesis test was non-significant, t(557.55) = 0.772, p = .441, given an alpha of 0.05. Based on the equivalence test and the null-hypothesis test combined, we can conclude that the observed effect is statistically not different from zero and statistically equivalent to zero.

4 Exploratory Analyses

4.1 3. Does dynamic norm (versus static or no norm) information increase participants’ positive attitude, intentions, and expectations to reduce their meat consumption?

4.1.1 Trace plots

Figure 4.1: Traceplots of regression parameters

Figure 4.2: Traceplots of regression parameters

Figure 4.3: Traceplots of regression parameters

4.1.2 Posterior plots

Figure 4.4: Posterior uncertainty intervals

Figure 4.5: Posterior density plot

4.1.3 Summary table

Table 4.1: Posterior results for simple model (H3)

	Model 1: Uninformative priors\(^a\)					Model 2: Weakly informative priors\(^b\)						Model 3: Moderately informative priors\(^c\)
	Mean (SD)	95% PPI	neff	PSRF	Prior	Mean (SD)	95% PPI	neff	PSRF	Bias (%)	Prior	Mean (SD)	95% PPI	neff	PSRF	Bias (%)	Prior
Interest	0.040 (0.156)	-0.265, 0.349	819.155	1.003	normal(0,10)	0.089 (0.154)	-0.212, 0.388	771.276	1.005	122.5	normal(0.5,0.75)	0.196 (0.132)	-0.059, 0.459	889.294	1.004	390	normal(0.5, 0.35)
Attitude	-0.049 (0.111)	-0.267, 0.165	771.073	1.004	normal(0,10)	-0.012 (0.112)	-0.234, 0.207	730.007	1.005	-75.51	normal(0.5,0.75)	0.064 (0.093)	-0.117, 0.244	928.977	1.003	-230.61	normal(0.5, 0.35)
Intention/Expectation	-0.036 (0.144)	-0.314, 0.251	808.662	1.005	normal(0,10)	0.011 (0.145)	-0.271, 0.291	725.346	1.006	-130.56	normal(0.5,0.75)	0.113 (0.122)	-0.12, 0.353	931.226	1.004	-413.89	normal(0.5, 0.35)
Note. PPI = posterior probability interval; PSRF = potential scale reduction factor; neff = effective sample size
a ppp = .499 b ppp = .493 c ppp = .437

4.2 4. How do demographic factors such as age, gender, and political position predict primary dependent variables relating to meat consumption?

4.2.1 Trace plots

Figure 4.6: Traceplots for estimated regression parameters

Figure 4.7: Traceplots for estimated regression parameters

Figure 4.8: Traceplots for estimated regression parameters

4.2.2 Posterior plots

Figure 4.9: Posterior uncertainty intervals

Figure 4.10: Posterior density plot

4.2.3 Summary table

Table 4.2: Posterior results for full model (H4)

	Model 1: Uninformative priors\(^a\)					Model 2: Informative priors\(^b\)						Model 3: Informative priors\(^c\)
Parameter	Mean (SD)	95% PPI	neff	PSRF	Prior	Mean (SD)	95% PPI	neff	PSRF	Bias (%)	Prior	Mean (SD)	95% PPI	neff	PSRF	Bias (%)	Prior
Interest
Condition	0.063 (0.147)	-0.211, 0.354	1166.766	1	normal(0,10)	0.100 (0.142)	-0.175, 0.372	1433.183	1.003	58.73	normal(0.5,0.75)	0.208 (0.130)	-0.048, 0.475	1593.563	1	230.16	normal(0.5,0.35)
Age	-0.002 (0.006)	-0.013, 0.009	1873.321	1.001	normal(0,10)	-0.002 (0.006)	-0.013, 0.009	1905.213	1.001	0	normal(0,10)	-0.002 (0.006)	-0.014, 0.009	1954.115	0.999	0	normal(0,10)
Gender	0.524 (0.158)	0.204, 0.826	1197.542	1.001	normal(0,10)	0.525 (0.154)	0.224, 0.83	1055.009	1.002	0.19	normal(0,10)	0.524 (0.156)	0.213, 0.821	1167.231	1.004	0	normal(0,10)
Politics	-0.308 (0.063)	-0.43, -0.186	1210.214	1	normal(0,10)	-0.311 (0.062)	-0.43, -0.189	1240.268	1.004	0.97	normal(0,10)	-0.312 (0.059)	-0.425, -0.197	1361.417	1.003	1.3	normal(0,10)
Attitudes
Condition	-0.033 (0.105)	-0.233, 0.176	1163.076	1	normal(0,10)	-0.008 (0.103)	-0.21, 0.191	1381.633	1.003	-75.76	normal(0.5,0.75)	0.071 (0.094)	-0.106, 0.26	1595.695	1.001	-315.15	normal(0.5,0.35)
Age	0.001 (0.004)	-0.007, 0.009	2020.368	1	normal(0,10)	0.001 (0.004)	-0.007, 0.009	2014.231	1.001	0	normal(0,10)	0.001 (0.004)	-0.008, 0.009	1924.127	1	0	normal(0,10)
Gender	0.299 (0.112)	0.081, 0.518	1252.849	1.002	normal(0,10)	0.299 (0.110)	0.088, 0.511	1086.499	1.002	0	normal(0,10)	0.299 (0.112)	0.078, 0.513	1193.494	1.004	0	normal(0,10)
Politics	-0.242 (0.045)	-0.328, -0.152	1135.188	1.001	normal(0,10)	-0.243 (0.045)	-0.331, -0.156	1255.325	1.004	0.41	normal(0,10)	-0.245 (0.043)	-0.327, -0.16	1354.755	1.001	1.24	normal(0,10)
Intentions
Condition	-0.018 (0.139)	-0.278, 0.252	1159.325	1	normal(0,10)	0.014 (0.136)	-0.255, 0.273	1470.680	1.003	-177.78	normal(0.5,0.75)	0.123 (0.124)	-0.117, 0.369	1452.978	1.002	-783.33	normal(0.5,0.35)
Age	0.008 (0.005)	-0.002, 0.018	1905.898	1.001	normal(0,10)	0.008 (0.005)	-0.002, 0.019	1952.531	1.001	0	normal(0,10)	0.008 (0.005)	-0.003, 0.018	1884.567	1	0	normal(0,10)
Gender	0.592 (0.148)	0.306, 0.88	1275.347	1.001	normal(0,10)	0.594 (0.145)	0.318, 0.881	1084.447	1.001	0.34	normal(0,10)	0.595 (0.147)	0.303, 0.874	1200.347	1.004	0.51	normal(0,10)
Politics	-0.272 (0.060)	-0.389, -0.156	1123.352	1.001	normal(0,10)	-0.274 (0.059)	-0.391, -0.158	1240.672	1.003	0.74	normal(0,10)	-0.275 (0.057)	-0.383, -0.161	1303.289	1.002	1.1	normal(0,10)
Expectations
Condition	0.063 (0.147)	-0.211, 0.354	1166.766	1	normal(0,10)	0.100 (0.142)	-0.175, 0.372	1433.183	1.003	58.73	normal(0.5,0.75)	3.148 (0.130)	-0.048, 0.475	1593.563	1	230.16	normal(0.5,0.35)
Age	-0.002 (0.006)	-0.013, 0.009	1873.321	1.001	normal(0,10)	-0.002 (0.006)	-0.013, 0.009	1905.213	1.001	0	normal(0,10)	1.614 (0.006)	-0.014, 0.009	1954.115	0.999	0	normal(0,10)
Gender	0.524 (0.158)	0.204, 0.826	1197.542	1.001	normal(0,10)	0.525 (0.154)	0.224, 0.83	1055.009	1.002	0.19	normal(0,10)	2.823 (0.156)	0.213, 0.821	1167.231	1.004	0	normal(0,10)
Politics	-0.308 (0.063)	-0.43, -0.186	1210.214	1	normal(0,10)	-0.311 (0.062)	-0.43, -0.189	1240.268	1.004	0.97	normal(0,10)	1.786 (0.059)	-0.425, -0.197	1361.417	1.003	1.3	normal(0,10)
Note. PPI = posterior probability interval; PSRF = potential scale reduction factor; neff = effective sample size
a ppp = .501 b ppp = .508 c ppp = .457

4.3 5. How does age interact with norm condition to influence dependent variables?

4.3.1 Trace plots

Figure 4.11: Traceplots of regression parameters

Figure 4.12: Traceplots of regression parameters

Figure 4.13: Traceplots of regression parameters

4.3.2 Summary table

Table 4.3: Posterior results for multi-sample analysis by age (H4)

	Model 1: Uninformative priors\(^a\)					Model 2: Informative priors\(^b\)						Model 3: Informative priors\(^c\)
Parameter	Mean (SD)	95% PPI	neff	PSRF	Prior	Mean (SD)	95% PPI	neff	PSRF	Bias (%)	Prior	Mean (SD)	95% PPI	neff	PSRF	Bias (%)	Prior
Older
Interest	0.127 (0.223)	-0.307, 0.553	917.266	1.002	normal(0,10)	0.200 (0.206)	-0.206, 0.601	930.554	1.001	57.48	normal(0.5,0.75)	0.362 (0.172)	0.041, 0.705	939.531	1.006	185.04	normal(0.5,0.35)
Attitude	0.035 (0.162)	-0.276, 0.361	978.182	1.004	normal(0,10)	0.086 (0.149)	-0.198, 0.375	961.888	1	145.71	normal(0.5,0.75)	0.208 (0.129)	-0.037, 0.463	933.096	1.01	494.29	normal(0.5,0.35)
Intention	-0.072 (0.214)	-0.508, 0.35	849.457	1.004	normal(0,10)	0.004 (0.194)	-0.376, 0.381	896.109	1	-105.56	normal(0.5,0.75)	0.170 (0.163)	-0.156, 0.489	902.042	1.008	-336.11	normal(0.5,0.35)
Expectation	0.027 (0.224)	-0.417, 0.456	838.891	1.002	normal(0,10)	0.120 (0.202)	-0.275, 0.508	867.831	1.002	344.44	normal(0.5,0.75)	0.288 (0.164)	-0.025, 0.606	963.833	1.003	966.67	normal(0.5,0.35)
Younger
Interest	-0.086 (0.160)	-0.4, 0.222	863.224	1.001	normal(0,10)	-0.019 (0.148)	-0.308, 0.271	910.991	1.002	-77.91	normal(0.5,0.75)	0.103 (0.120)	-0.135, 0.334	891.487	1.002	-219.77	normal(0.5,0.35)
Attitude	-0.012 (0.214)	-0.435, 0.402	846.377	1.001	normal(0,10)	0.073 (0.193)	-0.297, 0.457	891.293	1.002	-708.33	normal(0.5,0.75)	0.233 (0.157)	-0.075, 0.532	866.660	1.004	-2041.67	normal(0.5,0.35)
Intention	0.127 (0.223)	-0.307, 0.553	917.266	1.002	normal(0,10)	0.200 (0.206)	-0.206, 0.601	930.554	1.001	57.48	normal(0.5,0.75)	0.362 (0.172)	0.041, 0.705	939.531	1.006	185.04	normal(0.5,0.35)
Expectation	0.035 (0.162)	-0.276, 0.361	978.182	1.004	normal(0,10)	0.086 (0.149)	-0.198, 0.375	961.888	1	145.71	normal(0.5,0.75)	0.208 (0.129)	-0.037, 0.463	933.096	1.01	494.29	normal(0.5,0.35)
Note. PPI = posterior probability interval; PSRF = potential scale reduction factor; neff = effective sample size
a ppp = .480 b ppp = .494 c ppp = .415

5 Environment and data

5.0.1 Session information

## ─ Session info ───────────────────────────────────────────────────────────────
##  setting  value                       
##  version  R version 4.0.4 (2021-02-15)
##  os       macOS Big Sur 10.16         
##  system   x86_64, darwin17.0          
##  ui       X11                         
##  language (EN)                        
##  collate  en_GB.UTF-8                 
##  ctype    en_GB.UTF-8                 
##  tz       Europe/London               
##  date     2021-04-24                  
## 
## ─ Packages ───────────────────────────────────────────────────────────────────
##  package      * version    date       lib source                        
##  abind          1.4-5      2016-07-21 [1] CRAN (R 4.0.2)                
##  assertthat     0.2.1      2019-03-21 [1] CRAN (R 4.0.2)                
##  backports      1.2.1      2020-12-09 [1] CRAN (R 4.0.2)                
##  bayesplot      1.8.0      2021-01-10 [1] CRAN (R 4.0.2)                
##  bfrr         * 0.0.0.9000 2020-10-07 [1] Github (debruine/bfrr@9b80a99)
##  bitops         1.0-6      2013-08-17 [1] CRAN (R 4.0.2)                
##  bookdown       0.21       2020-10-13 [1] CRAN (R 4.0.2)                
##  boot           1.3-27     2021-02-12 [1] CRAN (R 4.0.2)                
##  broom          0.7.6      2021-04-05 [1] CRAN (R 4.0.2)                
##  bslib          0.2.4      2021-01-25 [1] CRAN (R 4.0.2)                
##  cachem         1.0.4      2021-02-13 [1] CRAN (R 4.0.2)                
##  callr          3.7.0      2021-04-20 [1] CRAN (R 4.0.4)                
##  car            3.0-10     2020-09-29 [1] CRAN (R 4.0.2)                
##  carData        3.0-4      2020-05-22 [1] CRAN (R 4.0.2)                
##  cellranger     1.1.0      2016-07-27 [1] CRAN (R 4.0.2)                
##  cli            2.4.0      2021-04-05 [1] CRAN (R 4.0.2)                
##  coda           0.19-4     2020-09-30 [1] CRAN (R 4.0.2)                
##  codebook     * 0.9.2      2020-06-06 [1] CRAN (R 4.0.2)                
##  colorspace     2.0-0      2020-11-11 [1] CRAN (R 4.0.3)                
##  crayon         1.4.1      2021-02-08 [1] CRAN (R 4.0.2)                
##  curl           4.3        2019-12-02 [1] CRAN (R 4.0.1)                
##  data.table     1.14.0     2021-02-21 [1] CRAN (R 4.0.2)                
##  DBI            1.1.1      2021-01-15 [1] CRAN (R 4.0.2)                
##  dbplyr         2.1.1      2021-04-06 [1] CRAN (R 4.0.2)                
##  desc           1.3.0      2021-03-05 [1] CRAN (R 4.0.2)                
##  devtools       2.4.0      2021-04-07 [1] CRAN (R 4.0.2)                
##  digest         0.6.27     2020-10-24 [1] CRAN (R 4.0.2)                
##  dplyr        * 1.0.5      2021-03-05 [1] CRAN (R 4.0.2)                
##  ellipsis       0.3.1      2020-05-15 [1] CRAN (R 4.0.2)                
##  emmeans      * 1.5.5-1    2021-03-21 [1] CRAN (R 4.0.2)                
##  estimability   1.3        2018-02-11 [1] CRAN (R 4.0.2)                
##  evaluate       0.14       2019-05-28 [1] CRAN (R 4.0.1)                
##  ez             4.4-0      2016-11-02 [1] CRAN (R 4.0.2)                
##  fansi          0.4.2      2021-01-15 [1] CRAN (R 4.0.2)                
##  farver         2.1.0      2021-02-28 [1] CRAN (R 4.0.2)                
##  fastmap        1.1.0      2021-01-25 [1] CRAN (R 4.0.2)                
##  forcats      * 0.5.1      2021-01-27 [1] CRAN (R 4.0.2)                
##  foreign        0.8-81     2020-12-22 [1] CRAN (R 4.0.4)                
##  fs             1.5.0      2020-07-31 [1] CRAN (R 4.0.2)                
##  generics       0.1.0      2020-10-31 [1] CRAN (R 4.0.2)                
##  ggplot2      * 3.3.3      2020-12-30 [1] CRAN (R 4.0.2)                
##  ggpubr       * 0.4.0      2020-06-27 [1] CRAN (R 4.0.2)                
##  ggridges       0.5.3      2021-01-08 [1] CRAN (R 4.0.2)                
##  ggsignif       0.6.1      2021-02-23 [1] CRAN (R 4.0.2)                
##  glue           1.4.2      2020-08-27 [1] CRAN (R 4.0.2)                
##  gridExtra      2.3        2017-09-09 [1] CRAN (R 4.0.2)                
##  gtable         0.3.0      2019-03-25 [1] CRAN (R 4.0.2)                
##  haven          2.4.0      2021-04-14 [1] CRAN (R 4.0.2)                
##  here         * 1.0.1      2020-12-13 [1] CRAN (R 4.0.2)                
##  highr          0.9        2021-04-16 [1] CRAN (R 4.0.2)                
##  hms            1.0.0      2021-01-13 [1] CRAN (R 4.0.2)                
##  htmltools      0.5.1.1    2021-01-22 [1] CRAN (R 4.0.2)                
##  httr           1.4.2      2020-07-20 [1] CRAN (R 4.0.2)                
##  insight        0.13.2     2021-04-01 [1] CRAN (R 4.0.2)                
##  jquerylib      0.1.3      2020-12-17 [1] CRAN (R 4.0.2)                
##  jsonlite       1.7.2      2020-12-09 [1] CRAN (R 4.0.2)                
##  kableExtra   * 1.3.4      2021-02-20 [1] CRAN (R 4.0.2)                
##  knitr        * 1.32       2021-04-14 [1] CRAN (R 4.0.2)                
##  labeling       0.4.2      2020-10-20 [1] CRAN (R 4.0.2)                
##  labelled       2.8.0      2021-03-08 [1] CRAN (R 4.0.2)                
##  lattice        0.20-41    2020-04-02 [1] CRAN (R 4.0.4)                
##  lifecycle      1.0.0      2021-02-15 [1] CRAN (R 4.0.2)                
##  lme4           1.1-26     2020-12-01 [1] CRAN (R 4.0.2)                
##  lubridate      1.7.10     2021-02-26 [1] CRAN (R 4.0.2)                
##  magrittr       2.0.1      2020-11-17 [1] CRAN (R 4.0.2)                
##  MASS         * 7.3-53.1   2021-02-12 [1] CRAN (R 4.0.2)                
##  Matrix         1.3-2      2021-01-06 [1] CRAN (R 4.0.4)                
##  MBESS          4.8.0      2020-08-05 [1] CRAN (R 4.0.2)                
##  memoise        2.0.0      2021-01-26 [1] CRAN (R 4.0.2)                
##  mgcv           1.8-35     2021-04-18 [1] CRAN (R 4.0.2)                
##  minqa          1.2.4      2014-10-09 [1] CRAN (R 4.0.2)                
##  mnormt         2.0.2      2020-09-01 [1] CRAN (R 4.0.2)                
##  modelr         0.1.8      2020-05-19 [1] CRAN (R 4.0.2)                
##  MOTE         * 1.0.2      2019-04-10 [1] CRAN (R 4.0.2)                
##  munsell        0.5.0      2018-06-12 [1] CRAN (R 4.0.2)                
##  mvtnorm        1.1-1      2020-06-09 [1] CRAN (R 4.0.2)                
##  nlme           3.1-152    2021-02-04 [1] CRAN (R 4.0.4)                
##  nloptr         1.2.2.2    2020-07-02 [1] CRAN (R 4.0.2)                
##  NLP          * 0.2-1      2020-10-14 [1] CRAN (R 4.0.2)                
##  openxlsx       4.2.3      2020-10-27 [1] CRAN (R 4.0.2)                
##  papaja       * 0.1.0.9997 2020-08-11 [1] Github (crsh/papaja@0457653)  
##  pillar         1.6.0      2021-04-13 [1] CRAN (R 4.0.2)                
##  pkgbuild       1.2.0      2020-12-15 [1] CRAN (R 4.0.2)                
##  pkgconfig      2.0.3      2019-09-22 [1] CRAN (R 4.0.2)                
##  pkgload        1.2.1      2021-04-06 [1] CRAN (R 4.0.2)                
##  plyr           1.8.6      2020-03-03 [1] CRAN (R 4.0.2)                
##  prettyunits    1.1.1      2020-01-24 [1] CRAN (R 4.0.2)                
##  processx       3.5.1      2021-04-04 [1] CRAN (R 4.0.2)                
##  ps             1.6.0      2021-02-28 [1] CRAN (R 4.0.2)                
##  psy          * 1.1        2012-06-21 [1] CRAN (R 4.0.2)                
##  psych        * 2.1.3      2021-03-27 [1] CRAN (R 4.0.2)                
##  purrr        * 0.3.4      2020-04-17 [1] CRAN (R 4.0.2)                
##  qualtRics    * 3.1.4      2021-01-14 [1] CRAN (R 4.0.2)                
##  R6             2.5.0      2020-10-28 [1] CRAN (R 4.0.2)                
##  RColorBrewer * 1.1-2      2014-12-07 [1] CRAN (R 4.0.2)                
##  Rcpp         * 1.0.6      2021-01-15 [1] CRAN (R 4.0.2)                
##  RCurl        * 1.98-1.3   2021-03-16 [1] CRAN (R 4.0.2)                
##  readr        * 1.4.0      2020-10-05 [1] CRAN (R 4.0.2)                
##  readxl         1.3.1      2019-03-13 [1] CRAN (R 4.0.2)                
##  remotes        2.3.0      2021-04-01 [1] CRAN (R 4.0.2)                
##  reprex         2.0.0      2021-04-02 [1] CRAN (R 4.0.2)                
##  reshape        0.8.8      2018-10-23 [1] CRAN (R 4.0.2)                
##  reshape2       1.4.4      2020-04-09 [1] CRAN (R 4.0.2)                
##  rio            0.5.26     2021-03-01 [1] CRAN (R 4.0.2)                
##  rlang        * 0.4.10     2020-12-30 [1] CRAN (R 4.0.2)                
##  rmarkdown      2.7        2021-02-19 [1] CRAN (R 4.0.2)                
##  rprojroot      2.0.2      2020-11-15 [1] CRAN (R 4.0.3)                
##  rstatix        0.7.0      2021-02-13 [1] CRAN (R 4.0.2)                
##  rstudioapi     0.13       2020-11-12 [1] CRAN (R 4.0.2)                
##  rvest          1.0.0      2021-03-09 [1] CRAN (R 4.0.2)                
##  sass           0.3.1      2021-01-24 [1] CRAN (R 4.0.2)                
##  scales         1.1.1      2020-05-11 [1] CRAN (R 4.0.2)                
##  sessioninfo    1.1.1      2018-11-05 [1] CRAN (R 4.0.2)                
##  sjlabelled     1.1.7      2020-09-24 [1] CRAN (R 4.0.2)                
##  slam           0.1-48     2020-12-03 [1] CRAN (R 4.0.2)                
##  SnowballC    * 0.7.0      2020-04-01 [1] CRAN (R 4.0.2)                
##  statmod        1.4.35     2020-10-19 [1] CRAN (R 4.0.2)                
##  stringi        1.5.3      2020-09-09 [1] CRAN (R 4.0.2)                
##  stringr      * 1.4.0      2019-02-10 [1] CRAN (R 4.0.2)                
##  svglite        2.0.0      2021-02-20 [1] CRAN (R 4.0.2)                
##  systemfonts    1.0.1      2021-02-09 [1] CRAN (R 4.0.2)                
##  testthat       3.0.2      2021-02-14 [1] CRAN (R 4.0.2)                
##  tibble       * 3.1.1      2021-04-18 [1] CRAN (R 4.0.2)                
##  tidyr        * 1.1.3      2021-03-03 [1] CRAN (R 4.0.2)                
##  tidyselect     1.1.0      2020-05-11 [1] CRAN (R 4.0.2)                
##  tidyverse    * 1.3.1      2021-04-15 [1] CRAN (R 4.0.2)                
##  tm           * 0.7-8      2020-11-18 [1] CRAN (R 4.0.2)                
##  tmvnsim        1.0-2      2016-12-15 [1] CRAN (R 4.0.2)                
##  TOSTER       * 0.3.4      2018-08-03 [1] CRAN (R 4.0.2)                
##  usethis        2.0.1      2021-02-10 [1] CRAN (R 4.0.2)                
##  utf8           1.2.1      2021-03-12 [1] CRAN (R 4.0.2)                
##  vctrs          0.3.7      2021-03-29 [1] CRAN (R 4.0.4)                
##  viridisLite    0.4.0      2021-04-13 [1] CRAN (R 4.0.2)                
##  webshot        0.5.2      2019-11-22 [1] CRAN (R 4.0.2)                
##  withr          2.4.2      2021-04-18 [1] CRAN (R 4.0.2)                
##  wordcloud    * 2.6        2018-08-24 [1] CRAN (R 4.0.2)                
##  xfun           0.22       2021-03-11 [1] CRAN (R 4.0.2)                
##  XML          * 3.99-0.6   2021-03-16 [1] CRAN (R 4.0.2)                
##  xml2           1.3.2      2020-04-23 [1] CRAN (R 4.0.2)                
##  xtable         1.8-4      2019-04-21 [1] CRAN (R 4.0.2)                
##  yaml           2.2.1      2020-02-01 [1] CRAN (R 4.0.2)                
##  zip            2.1.1      2020-08-27 [1] CRAN (R 4.0.2)                
## 
## [1] /Library/Frameworks/R.framework/Versions/4.0/Resources/library

5.0.2 Codebook

5.0.3 Metadata

5.0.3.1 Description

Dataset name: results

The dataset has N=846 rows and 37 columns. 0 rows have no missing values on any column.

Metadata for search engines

• Date published: 2021-04-24

x DYUK STUK UKNATION RESIDENT GENDER condition conditionbi genderbi agebi NATIONALITY INTEREST ATT1 ATT2 ATT3 INTENT1 INTENT2 INTENT3 EXPECT1 EXPECT2 EXPECT3 PERCEPTNUM PERCEPTSCALE CONSTRUAL_1 PERCEPTCHANGE PRECONFORMITY POLITICS AGE attitude_mean intention_mean expect_mean expintent_avg age_cent PERCEPTCHANGE_r comb_future No norm Static Dynamic

5.1 Codebook table

JSON-LD metadata

The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.

{
  "name": "results",
  "datePublished": "2021-04-24",
  "description": "The dataset has N=846 rows and 37 columns.\n0 rows have no missing values on any column.\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n|name            |label | n_missing|\n|:---------------|:-----|---------:|\n|DYUK            |NA    |       570|\n|STUK            |NA    |       562|\n|UKNATION        |NA    |        24|\n|RESIDENT        |NA    |         0|\n|GENDER          |NA    |         0|\n|condition       |NA    |         0|\n|conditionbi     |NA    |       286|\n|genderbi        |NA    |         3|\n|agebi           |NA    |         0|\n|NATIONALITY     |NA    |         0|\n|INTEREST        |NA    |         0|\n|ATT1            |NA    |         0|\n|ATT2            |NA    |         0|\n|ATT3            |NA    |         0|\n|INTENT1         |NA    |         0|\n|INTENT2         |NA    |         0|\n|INTENT3         |NA    |         0|\n|EXPECT1         |NA    |         0|\n|EXPECT2         |NA    |         0|\n|EXPECT3         |NA    |         0|\n|PERCEPTNUM      |NA    |         0|\n|PERCEPTSCALE    |NA    |         0|\n|CONSTRUAL_1     |NA    |         0|\n|PERCEPTCHANGE   |NA    |         0|\n|PRECONFORMITY   |NA    |         0|\n|POLITICS        |NA    |         0|\n|AGE             |NA    |         0|\n|attitude_mean   |NA    |         0|\n|intention_mean  |NA    |         0|\n|expect_mean     |NA    |         0|\n|expintent_avg   |NA    |         0|\n|age_cent        |NA    |         0|\n|PERCEPTCHANGE_r |NA    |         0|\n|comb_future     |NA    |         0|\n|No norm         |NA    |         0|\n|Static          |NA    |         0|\n|Dynamic         |NA    |         0|\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2).",
  "keywords": ["DYUK", "STUK", "UKNATION", "RESIDENT", "GENDER", "condition", "conditionbi", "genderbi", "agebi", "NATIONALITY", "INTEREST", "ATT1", "ATT2", "ATT3", "INTENT1", "INTENT2", "INTENT3", "EXPECT1", "EXPECT2", "EXPECT3", "PERCEPTNUM", "PERCEPTSCALE", "CONSTRUAL_1", "PERCEPTCHANGE", "PRECONFORMITY", "POLITICS", "AGE", "attitude_mean", "intention_mean", "expect_mean", "expintent_avg", "age_cent", "PERCEPTCHANGE_r", "comb_future", "No norm", "Static", "Dynamic"],
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "variableMeasured": [
    {
      "name": "DYUK",
      "description": "Dynamic UK",
      "@type": "propertyValue"
    },
    {
      "name": "STUK",
      "description": "Static UK",
      "@type": "propertyValue"
    },
    {
      "name": "UKNATION",
      "value": "1. 1,\n2. 2,\n3. 3,\n4. 4",
      "@type": "propertyValue"
    },
    {
      "name": "RESIDENT",
      "value": "1. 1,\n2. 2",
      "@type": "propertyValue"
    },
    {
      "name": "GENDER",
      "value": "1. 1,\n2. 2,\n3. 3",
      "@type": "propertyValue"
    },
    {
      "name": "condition",
      "value": "1. Dynamic,\n2. Static,\n3. No norm",
      "@type": "propertyValue"
    },
    {
      "name": "conditionbi",
      "value": "1. Dynamic,\n2. Static",
      "@type": "propertyValue"
    },
    {
      "name": "genderbi",
      "value": "1. 1,\n2. 2",
      "@type": "propertyValue"
    },
    {
      "name": "agebi",
      "value": "1. younger,\n2. older",
      "@type": "propertyValue"
    },
    {
      "name": "NATIONALITY",
      "description": "Nationality",
      "@type": "propertyValue"
    },
    {
      "name": "INTEREST",
      "@type": "propertyValue"
    },
    {
      "name": "ATT1",
      "@type": "propertyValue"
    },
    {
      "name": "ATT2",
      "@type": "propertyValue"
    },
    {
      "name": "ATT3",
      "@type": "propertyValue"
    },
    {
      "name": "INTENT1",
      "@type": "propertyValue"
    },
    {
      "name": "INTENT2",
      "@type": "propertyValue"
    },
    {
      "name": "INTENT3",
      "@type": "propertyValue"
    },
    {
      "name": "EXPECT1",
      "@type": "propertyValue"
    },
    {
      "name": "EXPECT2",
      "@type": "propertyValue"
    },
    {
      "name": "EXPECT3",
      "@type": "propertyValue"
    },
    {
      "name": "PERCEPTNUM",
      "@type": "propertyValue"
    },
    {
      "name": "PERCEPTSCALE",
      "@type": "propertyValue"
    },
    {
      "name": "CONSTRUAL_1",
      "@type": "propertyValue"
    },
    {
      "name": "PERCEPTCHANGE",
      "@type": "propertyValue"
    },
    {
      "name": "PRECONFORMITY",
      "@type": "propertyValue"
    },
    {
      "name": "POLITICS",
      "@type": "propertyValue"
    },
    {
      "name": "AGE",
      "@type": "propertyValue"
    },
    {
      "name": "attitude_mean",
      "@type": "propertyValue"
    },
    {
      "name": "intention_mean",
      "@type": "propertyValue"
    },
    {
      "name": "expect_mean",
      "@type": "propertyValue"
    },
    {
      "name": "expintent_avg",
      "@type": "propertyValue"
    },
    {
      "name": "age_cent",
      "@type": "propertyValue"
    },
    {
      "name": "PERCEPTCHANGE_r",
      "@type": "propertyValue"
    },
    {
      "name": "comb_future",
      "@type": "propertyValue"
    },
    {
      "name": "No norm",
      "@type": "propertyValue"
    },
    {
      "name": "Static",
      "@type": "propertyValue"
    },
    {
      "name": "Dynamic",
      "@type": "propertyValue"
    }
  ]
}`